



"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


UpperCamelCase     =							logging.get_logger(__name__)






class       UpperCamelCase__			(   _lowerCAmelCase      ):

			"""simple docstring"""


			A__	:					Dict							  =     ["pixel_values"]


			def __init__(      self						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = 1 / 255						,	SCREAMING_SNAKE_CASE__ = True						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = True						,	**SCREAMING_SNAKE_CASE__						,	)      ->	None:
				super().__init__(**SCREAMING_SNAKE_CASE__		)
				A__											=					size if size is not None else {"height": 384, "width": 384}
				A__											=					get_size_dict(SCREAMING_SNAKE_CASE__						,	default_to_square=SCREAMING_SNAKE_CASE__		)

				A__											=					do_resize
				A__											=					size
				A__											=					resample
				A__											=					do_rescale
				A__											=					rescale_factor
				A__											=					do_normalize
				A__											=					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
				A__											=					image_std if image_std is not None else OPENAI_CLIP_STD
				A__											=					do_convert_rgb


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = PILImageResampling.BICUBIC						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	np.ndarray:
				A__											=					get_size_dict(SCREAMING_SNAKE_CASE__						,	default_to_square=SCREAMING_SNAKE_CASE__		)
				if "height" not in size or "width" not in size:
					raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""		)
				A__											=					(size["height"], size["width"])
				return resize(SCREAMING_SNAKE_CASE__						,	size=SCREAMING_SNAKE_CASE__						,	resample=SCREAMING_SNAKE_CASE__						,	data_format=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	str:
				return rescale(SCREAMING_SNAKE_CASE__						,	scale=SCREAMING_SNAKE_CASE__						,	data_format=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	**SCREAMING_SNAKE_CASE__						,	)      ->	np.ndarray:
				return normalize(SCREAMING_SNAKE_CASE__						,	mean=SCREAMING_SNAKE_CASE__						,	std=SCREAMING_SNAKE_CASE__						,	data_format=SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = None						,	SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST						,	**SCREAMING_SNAKE_CASE__						,	)      ->	PIL.Image.Image:
				A__											=					do_resize if do_resize is not None else self.do_resize
				A__											=					resample if resample is not None else self.resample
				A__											=					do_rescale if do_rescale is not None else self.do_rescale
				A__											=					rescale_factor if rescale_factor is not None else self.rescale_factor
				A__											=					do_normalize if do_normalize is not None else self.do_normalize
				A__											=					image_mean if image_mean is not None else self.image_mean
				A__											=					image_std if image_std is not None else self.image_std
				A__											=					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

				A__											=					size if size is not None else self.size
				A__											=					get_size_dict(SCREAMING_SNAKE_CASE__						,	default_to_square=SCREAMING_SNAKE_CASE__		)

				A__											=					make_list_of_images(SCREAMING_SNAKE_CASE__		)

				if not valid_images(SCREAMING_SNAKE_CASE__		):
					raise ValueError(
					    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
					    "torch.Tensor, tf.Tensor or jax.ndarray."		)

				if do_resize and size is None or resample is None:
					raise ValueError("Size and resample must be specified if do_resize is True."		)

				if do_rescale and rescale_factor is None:
					raise ValueError("Rescale factor must be specified if do_rescale is True."		)

				if do_normalize and (image_mean is None or image_std is None):
					raise ValueError("Image mean and std must be specified if do_normalize is True."		)

				# PIL RGBA images are converted to RGB
				if do_convert_rgb:
					A__											=					[convert_to_rgb(SCREAMING_SNAKE_CASE__		) for image in images]

				# All transformations expect numpy arrays.
				A__											=					[to_numpy_array(SCREAMING_SNAKE_CASE__		) for image in images]

				if do_resize:
					A__											=					[self.resize(image=SCREAMING_SNAKE_CASE__						,	size=SCREAMING_SNAKE_CASE__						,	resample=SCREAMING_SNAKE_CASE__		) for image in images]

				if do_rescale:
					A__											=					[self.rescale(image=SCREAMING_SNAKE_CASE__						,	scale=SCREAMING_SNAKE_CASE__		) for image in images]

				if do_normalize:
					A__											=					[self.normalize(image=SCREAMING_SNAKE_CASE__						,	mean=SCREAMING_SNAKE_CASE__						,	std=SCREAMING_SNAKE_CASE__		) for image in images]

				A__											=					[to_channel_dimension_format(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		) for image in images]

				A__											=					BatchFeature(data={"pixel_values": images}						,	tensor_type=SCREAMING_SNAKE_CASE__		)

				return encoded_outputs





"""simple docstring"""


import random





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   dict:




						'''simple docstring'''



						lowerCamelCase__   ={i: [] for i in range(__lowerCAmelCase							)}

						# if probability is greater or equal than 1, then generate a complete graph
						if probability >= 1:
												return complete_graph(__lowerCAmelCase							)
						# if probability is lower or equal than 0, then return a graph without edges
						if probability <= 0:
												return graph

						# for each couple of nodes, add an edge from u to v
						# if the number randomly generated is greater than probability probability
						for i in range(__lowerCAmelCase							):
												for j in range(i + 1  ,       __lowerCAmelCase							):
																		if random.random() < probability:
																								graph[i].append(__lowerCAmelCase							)
																								if not directed:
																														# if the graph is undirected, add an edge in from j to i, either
																														graph[j].append(__lowerCAmelCase							)
						return graph





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   dict:




						'''simple docstring'''



						return {
						    i: [j for j in range(__lowerCAmelCase							) if i != j] for i in range(__lowerCAmelCase							)
						}


if __name__ == "__main__":
	import doctest

	doctest.testmod()


